The estimated equation is

Model 1: OLS, using observations 1950-1957 (T = 8)

Dependent variable: Stock

coefficient std. error t-ratio p-value

--------------------------------------------------------

const 225.000 6.21177 36.22 2.95e-08 ***

Time -6.25000 0.976086 -6.403 0.0007 ***

Mean dependent var 190.6250 S.D. dependent var 22.90313

Sum squared resid 468.7500 S.E. of regression 8.838835

R-squared 0.872340 Adjusted R-squared 0.851064

F(1, 6) 41.00000 P-value(F) 0.000684

Log-likelihood -27.63402 Akaike criterion 59.26804

Schwarz criterion 59.42692 Hannan-Quinn 58.19644

rho -0.010000 Durbin-Watson 1.650000

Now substitute time values in the estimated equation

225- 6.25 X

Give X values like 1 , 2 3 up to 10

You will get predicted values like this ( by manual calculation)

when X is 5 ( value for 1954 ) the estimated value is 193.75. When X is 6 ( value for 1955) the predicted value is 187.5. By OLS this is the answer.

On Sat, May 17, 2014 at 7:19 PM, Huffelpuff <huffelpuff420@gmail.com> wrote:

Hi,

I'm new to gretl, so forgive my ignorance. I'm aware that gretl provides
forecasting functionality, but I'm interested in using any of the
time-series models in gretl (AR, ARIMA, GARCH, etc) to interpolate a
section of unknown data. If I have something like this:

Year Stock value
1950 215
1951 220
1952 200
1953 195

Then it is easy in gretl to predict the following years (1954, 1955, and
so forth). But if I have something like this:

Year Stock value
1950 215
1951 220
1952 200
1953 195
1956 190
1957 185
1958 170
1959 150

I then want to "predict" (or technically interpolate) the values for the
years 1954 and 1955 (the stock value will probably be something between
195 and 190). Is this possible with gretl? If so, how?

Peter
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